


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_SCREAMING_SNAKE_CASE     :					Optional[int]      =       logging.getLogger(__name__)

@dataclass
class     A__   :







    """simple docstring"""

    __magic_name__         =						field(
        metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
    __magic_name__         =						field(
        default=snake_case__			,   metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
    __magic_name__         =						field(
        default=snake_case__			,   metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
    __magic_name__         =						field(
        default=snake_case__			,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}			,   )
    __magic_name__         =						field(default=snake_case__			,   metadata={'help': 'Whether tp freeze the encoder.'}      )
    __magic_name__         =						field(default=snake_case__			,   metadata={'help': 'Whether  to freeze the embeddings.'}      )






@dataclass
class     A__   :







    """simple docstring"""

    __magic_name__         =						field(
        metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}      )
    __magic_name__         =						field(
        default='summarization'			,   metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}			,   )
    __magic_name__         =						field(
        default=10_24			,   metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }			,   )
    __magic_name__         =						field(
        default=1_28			,   metadata={
            'help': (
                'The maximum total sequence length for target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }			,   )
    __magic_name__         =						field(
        default=1_42			,   metadata={
            'help': (
                'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded. '
                'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                'during ``evaluate`` and ``predict``.'
            )
        }			,   )
    __magic_name__         =						field(
        default=1_42			,   metadata={
            'help': (
                'The maximum total sequence length for test target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }			,   )
    __magic_name__         =						field(default=-1			,   metadata={'help': '# training examples. -1 means use all.'}      )
    __magic_name__         =						field(default=-1			,   metadata={'help': '# validation examples. -1 means use all.'}      )
    __magic_name__         =						field(default=-1			,   metadata={'help': '# test examples. -1 means use all.'}      )
    __magic_name__         =						field(default=snake_case__			,   metadata={'help': 'Source language id for translation.'}      )
    __magic_name__         =						field(default=snake_case__			,   metadata={'help': 'Target language id for translation.'}      )
    __magic_name__         =						field(default=snake_case__			,   metadata={'help': '# num_beams to use for evaluation.'}      )
    __magic_name__         =						field(
        default=snake_case__			,   metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}			,   )






def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     logger.info(F'''***** {split} metrics *****'''							)
     for key in sorted(metrics.keys()							):
          logger.info(F'''  {key} = {metrics[key]}'''							)
     save_json(UpperCamelCase_				,os.path.join(UpperCamelCase_				,F'''{split}_results.json'''							)							)
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)							)

     if len(sys.argv							) == 2 and sys.argv[1].endswith('''.json'''							):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          snake_case					,    snake_case					,    snake_case											=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
     else:
          snake_case					,    snake_case					,    snake_case											=							parser.parse_args_into_dataclasses()

     check_output_dir(UpperCamelCase_							)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''				,datefmt='''%m/%d/%Y %H:%M:%S'''				,level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,)
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''				,training_args.local_rank				,training_args.device				,training_args.n_gpu				,bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED							)				,training_args.fpaa				,)
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank							):
          transformers.utils.logging.set_verbosity_info()
     logger.info('''Training/evaluation parameters %s'''				,UpperCamelCase_							)

     # Set seed
     set_seed(training_args.seed							)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     snake_case											=							AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path				,cache_dir=model_args.cache_dir				,)

     snake_case											=							('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
     for p in extra_model_params:
          if getattr(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):
               assert hasattr(UpperCamelCase_				,UpperCamelCase_							), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
               setattr(UpperCamelCase_				,UpperCamelCase_				,getattr(UpperCamelCase_				,UpperCamelCase_							)							)

     snake_case											=							AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,cache_dir=model_args.cache_dir				,)
     snake_case											=							AutoModelForSeqaSeqLM.from_pretrained(
         model_args.model_name_or_path				,from_tf='''.ckpt''' in model_args.model_name_or_path				,config=UpperCamelCase_				,cache_dir=model_args.cache_dir				,)

     # use task specific params
     use_task_specific_params(UpperCamelCase_				,data_args.task							)

     # set num_beams for evaluation
     if data_args.eval_beams is None:
          snake_case											=							model.config.num_beams

     # set decoder_start_token_id for MBart
     if model.config.decoder_start_token_id is None and isinstance(UpperCamelCase_				,(MBartTokenizer, MBartTokenizerFast)							):
          assert (
              data_args.tgt_lang is not None and data_args.src_lang is not None
          ), "mBart requires --tgt_lang and --src_lang"
          if isinstance(UpperCamelCase_				,UpperCamelCase_							):
               snake_case											=							tokenizer.lang_code_to_id[data_args.tgt_lang]
          else:
               snake_case											=							tokenizer.convert_tokens_to_ids(data_args.tgt_lang							)

     if model_args.freeze_embeds:
          freeze_embeds(UpperCamelCase_							)
     if model_args.freeze_encoder:
          freeze_params(model.get_encoder()							)
          assert_all_frozen(model.get_encoder()							)

     snake_case											=							SeqaSeqDataset

     # Get datasets
     snake_case											=							(
         dataset_class(
             UpperCamelCase_				,type_path='''train'''				,data_dir=data_args.data_dir				,n_obs=data_args.n_train				,max_target_length=data_args.max_target_length				,max_source_length=data_args.max_source_length				,prefix=model.config.prefix or ''''''				,)
         if training_args.do_train
         else None
     )
     snake_case											=							(
         dataset_class(
             UpperCamelCase_				,type_path='''val'''				,data_dir=data_args.data_dir				,n_obs=data_args.n_val				,max_target_length=data_args.val_max_target_length				,max_source_length=data_args.max_source_length				,prefix=model.config.prefix or ''''''				,)
         if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
         else None
     )
     snake_case											=							(
         dataset_class(
             UpperCamelCase_				,type_path='''test'''				,data_dir=data_args.data_dir				,n_obs=data_args.n_test				,max_target_length=data_args.test_max_target_length				,max_source_length=data_args.max_source_length				,prefix=model.config.prefix or ''''''				,)
         if training_args.do_predict
         else None
     )

     # Initialize our Trainer
     snake_case											=							(
         build_compute_metrics_fn(data_args.task				,UpperCamelCase_							) if training_args.predict_with_generate else None
     )
     snake_case											=							SeqaSeqTrainer(
         model=UpperCamelCase_				,args=UpperCamelCase_				,data_args=UpperCamelCase_				,train_dataset=UpperCamelCase_				,eval_dataset=UpperCamelCase_				,data_collator=SeqaSeqDataCollator(
             UpperCamelCase_				,UpperCamelCase_				,model.config.decoder_start_token_id				,training_args.tpu_num_cores							)				,compute_metrics=UpperCamelCase_				,tokenizer=UpperCamelCase_				,)

     snake_case											=							{}
     # Training
     if training_args.do_train:
          logger.info('''*** Train ***'''							)

          snake_case											=							trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path							) else None							)
          snake_case											=							train_result.metrics
          snake_case											=							data_args.n_train

          trainer.save_model()  # this also saves the tokenizer

          if trainer.is_world_process_zero():
               handle_metrics('''train'''				,UpperCamelCase_				,training_args.output_dir							)
               all_metrics.update(UpperCamelCase_							)

               # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
               trainer.state.save_to_json(os.path.join(training_args.output_dir				,'''trainer_state.json'''							)							)

               # For convenience, we also re-save the tokenizer to the same directory,
               # so that you can share your model easily on huggingface.co/models =)
               tokenizer.save_pretrained(training_args.output_dir							)

    # Evaluation
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''							)

          snake_case											=							trainer.evaluate(metric_key_prefix='''val'''							)
          snake_case											=							data_args.n_val
          snake_case											=							round(metrics['''val_loss''']				,4							)

          if trainer.is_world_process_zero():
               handle_metrics('''val'''				,UpperCamelCase_				,training_args.output_dir							)
               all_metrics.update(UpperCamelCase_							)

     if training_args.do_predict:
          logger.info('''*** Predict ***'''							)

          snake_case											=							trainer.predict(test_dataset=UpperCamelCase_				,metric_key_prefix='''test'''							)
          snake_case											=							test_output.metrics
          snake_case											=							data_args.n_test

          if trainer.is_world_process_zero():
               snake_case											=							round(metrics['''test_loss''']				,4							)
               handle_metrics('''test'''				,UpperCamelCase_				,training_args.output_dir							)
               all_metrics.update(UpperCamelCase_							)

               if training_args.predict_with_generate:
                    snake_case											=							tokenizer.batch_decode(
                        test_output.predictions				,skip_special_tokens=UpperCamelCase_				,clean_up_tokenization_spaces=UpperCamelCase_							)
                    snake_case											=							lmap(str.strip				,UpperCamelCase_							)
                    write_txt_file(UpperCamelCase_				,os.path.join(training_args.output_dir				,'''test_generations.txt'''							)							)

     if trainer.is_world_process_zero():
          save_json(UpperCamelCase_				,os.path.join(training_args.output_dir				,'''all_results.json'''							)							)

     return all_metrics
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     main()


if __name__ == "__main__":
 main()








class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case       ):
         snake_case											=							n
         snake_case											=							[None] * self.n
         snake_case											=							0  # index of the first element
         snake_case											=							0
         snake_case											=							0
    def __len__(			self       ):
         return self.size
    def 						a_     (			self       ):
         return self.size == 0
    def 						a_     (			self       ):
         return False if self.is_empty() else self.array[self.front]
    def 						a_     (			self     ,   __snake_case       ):
         if self.size >= self.n:
              raise Exception('''QUEUE IS FULL'''       )

         snake_case											=							data
         snake_case											=							(self.rear + 1) % self.n
         self.size += 1
         return self




    def 						a_     (			self       ):
         if self.size == 0:
              raise Exception('''UNDERFLOW'''       )

         snake_case											=							self.array[self.front]
         snake_case											=							None
         snake_case											=							(self.front + 1) % self.n
         self.size -= 1
         return temp




